Stanford Seminar - Objects, Skills, and the Quest for Compositional Robot Autonomy

This research seminar examines how abstraction, composition, and scalable systems can support robust robot autonomy. It presents methods for learning object representations through embodied interaction and combining sensorimotor skills for long-horizon tasks.

Syllabus

Introduction
James Webb Space Telescope
Robot Learning Workflow
Complex vs Reliable
Abstraction and Composition
System Perspective
Compositional Robot Autonomy Stack
Neural Task Programming
Robotic Grasping
Characterization of Objects
GIGA
Neural Fields
Supervised Procedure
Real Reward Experiments
Body Interaction
Physical Interaction
Concrete Approach
Interactive Digital Training
Questions
First Bus
Work is First
Conclusion
Classroom
Context Principle
Maple
Grasping
Action Space
Atomic Primitives
Task Sketch
Conclusions
What we learned
Skill
AI Architecture
New Frontier
Questions and Answers